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here’s a bumper sticker bon mot popular among techies that reads something like this: “We have lots of data but no information.” Some call it metadata or use the now overused term “Big Data.”

Within healthcare facilities but outside of the healthcare information technology clique, the concept of data analytics seems to be the trendiest buzzword to capture the ears and eyes of non-IT administrators and clinicians alike – the C-Suite senior-level executives, potentially including Supply Chain, and physicians and surgeons. As a result, payers, providers and suppliers (including ser- vice companies) alternately are buying into a concept loosely coined and paraphrased from the volume of press materials in circulation: “We are promoting, or striving to implement, best practices for leveraging clinical data analytics to predict

improving care and lowering costs, but it’s not. In order to drive quality care at lower costs, provider organizations must fi rst gain insight into the activity happening across their care team and patient populations, and then work to identify where big savings can be found — by reducing overutilization of services, maximizing contractual gains or improving coding and billing practices. Fundamental operational explorations and changes like this don’t require “big clinical data” analysis, but can contribute much higher immediate value. However, for large health systems that have already addressed operational pain points for fi nancial gain and have budgets large enough to invest in tools that lever- age big clinical data, predictive analytics is worth it because in most cases these organizations have bigger budgets to explore with and to mine data for clinical advantage. But for many, this is not the case.

8 December 2013

Leveraging clinical data analytics for quality care at lower costs can be accomplished in many ways. 1. Predictive analytics can be applied to identify those indi- viduals within a population that are most likely to respond to care management interventions.

2. Clinical data analytics can be leveraged to measure trends in disease prevalence and population risk profi les.

outcomes, measure trends and establish correlations that drive quality care at lower costs.” It’s one of those overarching concepts that sounds great on paper and in Powerpoint presentations but can be confusing and cumbersome to put into practice. In short, the strategy starts with the best of intentions and then detours into creatively but quirkily managed and organized anarchy. Still, the concept, which supports the framework for population health, remains noble and worthy of pursuit. Yet questions linger about what this concept truly means, whether we’re collecting too much, using too little and wasting time and money in the process. Health Management Technology reached out to group of executives in the data analytics space to clear up some of the fog.

In short, to address cost eff ectiveness of care delivery an organization needs more than clinical data, it needs claims data and a clear view on things like provider productivity, schedul- ing trends, payer mix, coding patterns, etc. – things that weigh heavily on fi nancial performance in today’s healthcare landscape. It’s most important for practices to understand areas for fi nancial and workfl ow improvement, and then bring these learnings back to the point of care.